While the assumption of the underlying model (in prediction of repayment rate) to be linear for regression methods might be correct, the study also explored utilization of non-linear models such as Random Forest [1]}. Random forests is an ensemble learning method, that constructs a multitude of decision trees [2]} during training to fit the data, and can have a categorical or a continuous value output. For example, the model is capable of supervised learning with prediction of classes of outcome, and similarly can output prediction values (mean values using regression) from the observations. Furthermore, the algorithm is also capable of identifying important features which influence the decision of the models. Thus, prediction models based on Random Forest were developed in the study.
